Color gamut mapping/enhancement technique using skin color detection

ABSTRACT

A method for mapping/enhancing the color of an image to be displayed on a display includes receiving an image having a plurality of pixels where each of the pixels has a plurality of color components. The image is processed using a pair of gamut color mapping operations in combination with skin-tone pixels detection to modify the image in a suitable manner for presentation on the display.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional App. No.60/879,644, filed Jan. 9, 2007.

BACKGROUND OF THE INVENTION

The present invention relates to display color gamut mapping and imagecolor enhancement.

An image color enhancement algorithm maps color of an image to new moresaturated colors. Image color enhancement is also a three dimensionalmapping technique. The input of color enhancement is 3 dimensional andthe output is 3 dimensional.

The advance of flat panel display (FPD) technology is able to make thecolor gamut of a display wider than the sRGB/ITU-R BT.709 color gamutthat is widely used by the current HDTV and Internet/computersstandards. A color gamut mapping algorithm (GMA) maps RGB (red, blue,green) display values of a color in a color gamut to new RGB values in anew gamut. The RGB display values before and after a GMA usually aredifferent, and may or may not represent the same physical color. Theinput of a GMA is also 3 dimensional and the output of a GMA is also 3dimensional.

A GMA from small gamut to big gamut is an image color enhancementalgorithm, and has the same challenges as an image color enhancementalgorithm without a display gamut change Most existing current imagecolor enhancement techniques typically boost saturation of colors whilekeeping the colors' hue substantially unchanged. In the hue-saturationcolor wheel such as the one shown in FIG. 1, a typical color enhancementtechnique moves colors outward on the radial direction as shown by thearrows. Essentially, the color enhancement algorithm increases the inputimages' dynamic range by increasing the color saturation of the pixels.

The techniques used to enhance the color enhancement of an image arebased upon modification of individual pixels. When the color of a pixelis enhanced to a new color, the conversion from the old color to the newcolor for each pixel is a predetermined fixed adjustment for the entireimage or for the entire video.

By way of example, televisions have built-in color enhancementtechniques to enhance unsaturated colors in certain content and letviewers set their color preferences. Because the human eye is verysensitive to the skin color, it is important for a color enhancementtechnique to render skin colors properly. If they are essentiallycalibrated at the input, then they are generally not increased insaturation. Preventing this change in saturation of skin colors can becalled protection from saturation or simply saturation. It is alsodesirable for a color enhancement technique to separately adjust skincolors and non-skin colors using different characteristics.

Some color enhancement techniques have the capability of protecting skincolors. These techniques are typically pixel-based. When the color of apixel is enhanced to a new color, the conversion from the old color tothe new color is fixed, and is not affected by other pixels. Becausepixel-based color enhancement techniques with skin color protectioncannot overcome the issue that the colors of skin and non-skin arehighly overlapped, these techniques cannot effectively protect skintones to maintain their calibration with the input image.

The pixel-based algorithms do not work effectively. Specifically, toavoid generating visible contouring artifacts in the areas of an imagewhere skin and neighboring non-skin colors are mixed, both the skincolor region in the color space and the gradual transition regionbetween the skin color region and the non-skin color region have to beset very wide. Typically, the skin color and transition regions covernearly half of the color gamut, as illustrated in FIG. 2. On the otherhand, some true skin colors are missed in the skin color region andtherefore remain unprotected. Consequently, many non-skin colors areimproperly protected while many skin colors are improperly enhanced bythe enhancement techniques.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates two adjacent colors in the hue-saturation color wheelthat are not adjacent in the wheel after color enhancement.

FIG. 2 illustrates the actual skin color region and the skin regiondefined by a pixel based technique in a color wheel.

FIG. 3 illustrates a block diagram of the proposed technique.

FIG. 4 illustrates a look up table of skin color.

FIG. 5 illustrates a color wheel.

FIG. 6 illustrates a color gamut in the x-y chromaticity chart and skinscores.

FIG. 7 illustrates a skin color-cognizant gamut mapping apparatus withtwo channel decomposition.

FIG. 8 illustrates a 2D LUT.

FIG. 9 illustrates a color gamut and skin score.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT

An observation was made that a typical pixel based GMA/color enhancementtechnique results in two similar pixel colors before enhancement beingmodified to different values that are significantly less similar afterenhancement. FIG. 1 illustrates two different situations. Situation 1illustrates the case when two pixel colors are similar in saturation buthave different hues, and situation 2 illustrates the case when twocolors have the same hue and similar saturations.

In both situations, the two pixel colors are close to each other in thecolor wheel before GMA/color enhancement. The two colors are spacedsignificantly apart from each other in the color wheel after colorenhancement, indicating that the two enhanced colors are less similarafter GMA/enhancement than they were before GMA/enhancement.

As a result, single pixel-based GMA/color enhancement techniques alsoenhance artifacts while it enhances colors. The pixels in spatial flatareas of the non-enhanced image tend to have similar colors, and thedifferences among the similar colors are not very visible to the viewer.Because the pixel-based GMA/color enhancement techniques enlarge thedifferences of similar colors, the resulting differences of the enhancedimage may become very visible, and consequently a flat area of the imagebefore GMA/enhancement may not be very flat anymore afterGMA/enhancement. Specifically, pixel-based color GMA/enhancementtechniques are prone to amplifying noise and structural artifacts,generally referred to as noise, that is otherwise generally unobservablein the flat area to become readily observable after color enhancement.Also, the pixel-based color GMA/enhancement technique tends to amplifyand generate quantization artifacts in the smooth regions beforeGMA/enhancement that become relatively rough after GMA/enhancement. Inaddition, amplifying compression artifacts that are generallyunobservable in the non-enhanced image become generally noticeable afterGMA/enhancement. The compression artifacts include, for example,contours, which are typically due to insufficient bit-depth, blockyartifacts, which are common for block-based compression schemes, andringing artifacts, which are due to loss of high frequency caused bycompression.

The GMA/color enhancement technique results in an increased colorsaturation for an image, and as a result tends to increase the noise andartifacts that are in the image, which are often not observable at thelower dynamic range. Accordingly, it is desirable to reduce thegeneration of artifacts while enhancing the color of the image with anincreased saturation or dynamic range. While decreasing the generationof artifacts in the increased dynamic range image, the technique shouldalso preserve image details which are generally high frequency in natureand akin to ‘noise’.

A skin-cognizant GMA/color enhancement is a particular GMA/colorenhancement that may include one or more of the followingcharacteristics:

-   -   rendering skin colors faithfully;    -   utilizing the expanded color gamut for GMA and/or enhancing        non-skin colors for color enhancement;    -   reducing the generation of contouring or other artifacts; and a        flexible implementation.        It is noted that in the code value space the GMA function and        the color enhancement function for protecting skin tones have        opposite effects, namely decreasing or increasing the code        values.

To fulfill these characteristics, a skin-cognizant GMA/color enhancementtechnique may be based upon a pair (or more) of GMA/color enhancementalgorithms. One conservative GMA/color enhancement algorithm (referredto herein as GMA0) may substantially reproduce the input physicalcolors, while the other aggressive GMA/color enhancement algorithm(referred to herein as GMA1) may utilize a greater extent of theexpanded color gamut or aggressively enhance the input colors. Both ofthese GMAs are usually designed in such a manner that they operateindependent of skin colors. In addition, a skin-cognizant operator maybe used together with the pair of GMAs to produce a final mapping fromone color space to another color space or enhancement while maintainingskin tones with a suitable set of colors.

The block diagram of a skin-cognizant GMA/color enhancement is shown inFIG. 3. First, the technique calculates a skin score 300 of the inputcolor 302. Then, the skin score 300 is spatially filtered by an 8-tapIIR spatial filter 304 (for example) to reduce contouring artifacts. Atthe same time, the conservative GMA0 306 and aggressive GMA1 308 arecomputed. Finally, the outputs of the two GMAs 306 and 308 are blended310 together in accordance with the output of the filtering 304 in amanner that protects the skin tones of the image (described in detaillater).

To protect skin colors during blending 310, it is desirable to identifywhich color is the skin color. The system may assign a scalar between 0and 1 to every color to give a “score” 312 on the likelihood this coloris a skin color. This scalar value may be referred to herein as a “skinscore”. Specifically, for skin score, 0 is a confident non-skin color, 1is a confident skin color, and larger than 0 and smaller than 1indicates a range of skin color confidence.

There are two reasons it is preferable to use a non-binary skin scoreindicator. First, the skin and non-skin colors are highly overlapped,especially due to the effects of lighting. While some colors are morelikely to be skin colors than other colors, there is not any devotedskin colors, so the best way to describe skin colors is a scalar.Second, a scalar provides a smooth transition from skin colors andnon-skin colors in the color space to reduce generating contouringartifacts in the image.

Based on the skin score 312, in the skin-cognizant GMA, a mapped coloris a blending of the colors mapped by two GMAs, namely, GMA0 306 andGMA1 308. GMA0 is a conservative GMA that substantially or exactlyreproduces the input color space (such as sRGB skin tones) in the widercolor gamut of the display and therefore acts to “protect” skin colors.GMA1 is an aggressive GMA1 that stretches the input color space of theinput colors (such as sRGB non-skin tones) to the wide color gamut.Mathematically, the blending may be expressed as:y=GMA(c)=skinScore(c)*GMA0(c)+(1−skinScore(c)*GMA1(c)  (1)

where skinScore (c) is the skin core of the input color c.

A linear mapping approach in GMA0 may be used to reproduce the sRGBcolor in the wide color gamut. Specifically, the input RGB may first gothrough sRGB gamma mapping to a linear luminance domain, multiplied bythe 3×3 conversion matrix, and modified back from the linear domain tothe non-linear domain by inverse sRGB gamma mapping. Note that if sRGBis not 100% inside the new wide color gamut, then negative components inthe 3×3 conversion matrix may occur when the gamut is reduced relativeto the code values.

GMA1 may be an advanced GMA that makes use of all or a substantial partof the expanded wide color gamut. The system may permit the input imageto go through without any added processing, if desired. In this case,the wider color gamut of the more saturated primaries will stretch allcolors processed by GMA1 to be more saturated. To further reduce thepossible contouring artifacts happening, the system may spatially filter304 the skin score of pixel with its neighbors. After this filtering,the skin score is not only smooth in the color space but also smootherspatially.

The skin score 312 may be adjusted by a set of factors. The skin scoreand skin color probability are preferably scalars between 0 and 1, andskin score is based on skin color probability, but the system shouldmodify this value for a more accurate determination of the skin score.Other factors can be used to adjust the skin score.

The gain control may be determined by setting a scalar k_gain 350between 0 and 1 to control the “gain” of skin score, according to theviewer preference and wide color gamut. Specifically, the system maymodify skin score as:skinScore_(new) =k_gain*skinScore 0≦k≦1  (2)

This skinScore_(new) may be plugged into equation 1. The parameter k iscontrolled by the viewers. If k_gain is set to 1, thenskinScore_(new)=skinScore; if k is set to 0, then skinScore_(new)=0, andthe result is the aggressive GMA y=GMA1(c) for all pixels, since non areconsidered skin codes to be processed by GMA0.

One may modify a parameter to adjust the saturation. One may set ascalar k_sat 360 between 0 and 2 to control the range of skin colors onthe saturation axis. All the saturation values prior to sending to theskin score look up tables are first multiplied with k_sat. The defaultk_sat is 1. When k_sat is smaller than 1, the range of skin colors onthe saturation axis is increased; when is bigger than 1, the range ofskin colors on the saturation axis is reduced.

One may modify a parameter to adjust the hue of the skin color region inthe color space. One may set a scalar k_hue 370 between 0 and 2 tocontrol the range of skin colors on the saturation axis. All the huevalues prior to sending to the skin score look up tables are firstmultiplied with k_hue. The default setting is 1. When k_hue is smallerthan 1, the range of skin colors on the hue axis is increased; when isbigger than 1, the range of skin colors on the hue axis is reduced.

A test set of skin color probability is shown in the FIG. 4. The skincolor probability distribution provides baseline information for roughlydeciding the location and size that the skin color region should be inthe color space. From the skin color probability shown in FIG. 4, it maybe observed that most skin colors are R>G>B or R>B>G, and those close tothe neutral have higher probability. Therefore, skin score should bepositive when R>G>B or R>B>G, and are bigger when a color is close tothe neutral.

The skin score is also affected by the target wide color gamut. Becausethe skin and non-skin colors are highly overlapped, many colors are noteither 100% skin colors or 100% non-skin color with skin score biggerthan 0 and smaller than 1. Equation (1) shows that the skin-cognizantalgorithm maps these colors as the mixture of GMA0 and GMA1 weighted byskin score 312. GMA0 is the sRGB reproduction in the new expanded colorgamut, which is independent from the expanded color gamut oncedisplayed. The GMA0 technique parameters, however, do depend on thecolor gamut primary values. GMA1 is dependent on the expanded colorgamut. Therefore, if the system makes skin score 312 independent fromthe expanded wide color gamut, then the mapped colors code values changewhen the expanded wide color gamut changes. The measured colors on thedisplay do not change. On the other hand, if the system wants the mappedcolors to be relatively constant when the expanded wide color gamutchanges, then the skin score may be adjusted according to different widecolor gamuts.

Skin score may be adjustable to different viewer groups as well. Someviewer group prefers more saturated colors than the others and adjustingskin score can fulfill this preference. The skin score is the functionof RGB. Therefore, the form of this function may be chosen to make theskin score easily adjustable on the wide color gamut and viewerpreference.

Skin score may be stored and calculated by a look-table (LUT) in theskin-cognizant algorithm. If the system directly uses RGB color space,the LUT would be 3 dimensional. It is complex to make a 3D LUTadjustable, and therefore 3D LUT is problematic to use for theadjustability that the may be desired in the skin scores. Therefore, thesystem may directly use RGB color space and the 3D LUT is not highlydesirable.

To simplify the determination, the skin score conceptually may bemodeled in a modified HSV color space. Then one 3D LUT could be replacedby three 2D LUTs and two 1D LUTs. Several smaller 2D LUTs and 1D LUTsare easier to adjust than one big 3D LUT.

First the system divides the RGB color space into six areas, thendefines hue (H), saturation (S) and value (V) separately in the sixareas (note that while S and V are the standard definition, H isdifferent), and finally the system defines the skin score for each area.First the red-yellow area is discussed in detail and then the remainingfive areas. The six color areas and S and H are illustrated in FIG. 5.

All the colors in the red-yellow area 510 are either reddish oryellowish. The saturation, hue and value are defined as:

$S = \frac{r - b}{r}$ $H = \frac{g - b}{r - b}$ V = r

S and H are between 0 and 1. When S is 0, r=g=b and the color is thewhite; when S is 1, b is 0 and the color is most saturated. When H is 0,g=b and the color is red; when H is 1, g=b and the color is yellow.

It has been determined that skin score can be well modeled by thefollowing equation:

${{skinScore}_{RY}\left( {H,S,V} \right)} = \left\{ \begin{matrix}{f_{RY}\left( {S,H} \right)} & {{when}\mspace{14mu} V\mspace{14mu}{is}\mspace{14mu}{not}\mspace{14mu}{too}\mspace{14mu}{small}\mspace{14mu}{or}\mspace{14mu}{too}\mspace{14mu}{big}} \\{f_{RY}\left( {{S*k_{s}},{H*k_{H}}} \right)} & {{when}\mspace{14mu} V\mspace{14mu}{is}\mspace{14mu}{too}\mspace{14mu}{small}\mspace{14mu}{or}\mspace{14mu}{too}\mspace{14mu}{big}}\end{matrix} \right.$

where k_(S) and k_(H) vary with V.

The system can use RGB to represent the above equation as:skinScore=f _(RY)((r−b)*p(r),(g−b)*q(r−b))  (2)

The above equation can be implemented by concatenation of 1D LUTs and 2DLUTs. The calculation uses one 2D LUT for f_(RY)(·,·), and two 1D LUTsfor p(·) and q(·).

The 2D LUT f_(RY)(·,·) is illustrated in FIG. 6.

The red-magenta (red>blue>green) area 520 may be represented as follows:

$\begin{matrix}{{S = \frac{r - g}{r}}{H = \frac{b - g}{r - g}}{V = r}{{{skinScore}_{RM}\left( {H,S,V} \right)} = {f_{RM}\left( {{\left( {r - g} \right)*{p(r)}},{\left( {b - g} \right)*{q\left( {r - g} \right)}}} \right)}}} & (3)\end{matrix}$

The calculation uses a new 2D LUT for f_(RM)(·,·) and the same two 1DLUTs for p(·) and q(·).

The calculation for the blue-magenta (blue>red>green) area 530 may bedefined as follows:

$\begin{matrix}{{S = \frac{b - g}{g}}{H = \frac{r - g}{b - g}}{V = b}{{{skinScore}_{BM}\left( {H,S,V} \right)} = {f_{BM}\left( {{\left( {b - g} \right)*{p(b)}},{\left( {r - g} \right)*{q\left( {b - g} \right)}}} \right)}}} & (4)\end{matrix}$

The calculation uses a new 2D LUT for f_(BM)(·,·) and the same two 1DLUTs for p(·) and q(·).

The calculation for the blue-cyan (blue>green>red) area 540 may becharacterized as follows:

$\begin{matrix}{{S = \frac{b - r}{b}}{H = \frac{g - r}{b - r}}{V = b}{{{skinScore}_{BC}\left( {H,S,V} \right)} = {f_{BC}\left( {{\left( {b - r} \right)*{p(b)}},{\left( {g - r} \right)*{q\left( {b - r} \right)}}} \right)}}} & (5)\end{matrix}$

The calculation uses a 2D LUT for f_(BC)(·,·)=f_(BM)(·,·) and the sametwo 1D LUTs for p(·) and q(·).

The 2D LUT f_(BC)(·,·) is illustrated in FIG. 8. In this case,f_(BD)(·,·) and f_(BM)(·,·) are the same.

The calculation for the green-cyan (green>blue>red) area 550 may becharacterized as follows:

$\begin{matrix}{{S = \frac{g - r}{g}}{H = \frac{b - r}{g - r}}{V = g}{{{skinScore}_{GC}\left( {H,S,V} \right)} = {f_{GC}\left( {{\left( {g - r} \right)*{p(g)}},{\left( {b - r} \right)*{q\left( {g - r} \right)}}} \right)}}} & (6)\end{matrix}$

The calculation uses a 2D LUT for f_(BC)(·,·)=f_(GC)(·,·) and the sametwo 1D LUTs for p(·) and q(·).

The 2D LUT f_(GC)(·,·) is illustrated in FIG. 8. In this case,f_(GC)(·,·) and f_(BM)(·,·) are the same.

The calculation for the green-yellow (green>red>blue) area 560 may becharacterized as follows:

$\begin{matrix}{{S = \frac{g - b}{g}}{H = \frac{r - b}{g - b}}{V = g}{{{skinScore}_{GY}\left( {H,S,V} \right)} = {f_{GY}\left( {{\left( {g - b} \right)*{p(g)}},{\left( {r - b} \right)*{q\left( {g - b} \right)}}} \right)}}} & (7)\end{matrix}$

The calculation uses a 2D LUT for f_(BC)(·,·)=f_(GY)(·,·) and the sametwo 1D LUTs for p(·) and q(·).

The skin score for the sRGB color gamut is illustrated in 2D in FIG. 6.One may notice most skin colors are in the red-yellow and red-magentaareas and close to the white point.

Calculating skin score may use both a liner buffer and LUTs. Aspreviously shown, the algorithm divides the color space into six colorareas, and each of the six color areas uses one 2D LUT and two 1D LUTs.Because all the six color areas share the same 1D LUTs to compute p(·)and q(·), totally there are two 1D LUTs. While the red-yellow andred-magenta color areas use their own 2D LUTs, and the rest four colorareas share one 2D LUTs, totally there are three 2D LUTs.

GMA0 may use two 1D LUTs for gamma correction and inverse gammacorrection. The IIR filter for skin score requires one line buffer forstoring previous pixels' skin scores.

One may use a filter to smooth skin score in order to prevent anypotential contouring artifacts. To reduce the hardware cost, the filteris chosen as an IIR filter. Specifically, the formula is

$\begin{matrix}{{{skin}\mspace{14mu}{score}\mspace{14mu}\left( {x,y} \right)} = {{a_{0}*{skin}\mspace{14mu}{score}\mspace{14mu}\left( {x,y} \right)} + {a_{1}*{skin}\mspace{14mu}{score}\mspace{14mu}\left( {{x - 1},y} \right)} + {a_{2}*{skin}\mspace{14mu}{score}\mspace{14mu}\left( {{x - 2},y} \right)} + {a_{3}*{skin}\mspace{14mu}{score}\mspace{14mu}\left( {{x - 2},{y - 1}} \right)} + {a_{4}*{skin}\mspace{14mu}{{score}\left( {{x - 1},{y - 1}} \right)}} + {a_{5}*{skin}\mspace{14mu}{score}\mspace{14mu}\left( {x,{y - 1}} \right)} + {a_{6}*{skin}\mspace{14mu}{score}\mspace{14mu}\left( {{x + 1},{y - 1}} \right)} + {a_{7}*{skin}\mspace{14mu}{score}\mspace{14mu}\left( {{x + 2},{y - 1}} \right)}}} & (9)\end{matrix}$

where x is the row index, y is the column index, and a₀+a₁+a₂+a₃+a₄=1.

This IIR filter uses one line buffer for skin score, but does notrequire line buffers for RGB.

In order to reduce the artifacts resulting from GMA/image enhancement, amodified technique may incorporate spatial information with theGMA/color enhancement. In addition, the spatial information may beobtained using multi-channel decomposition of the image. Morespecifically, the preferred technique may decompose an image intomultiple images. The one image may incorporate a pixel-based GMA/colorenhancement technique. The color enhanced image and the non-enhancedimage are then combined back into a single image.

Referring to FIG. 7, specifically, the input image 700 is firstdecomposed into lowpass 710 and highpass 720 images by a sigma filter730. The lowpass image, containing no details or artifacts, goes throughthe GMA 740. The highpass image, containing details and noise andartifacts, does not go through the GMA and will be added 760 back to thecolor mapped lowpass image 750 to generate the new image 770. Therefore,the noise in the highpass image 720 is not enhanced by the GMA. Inaddition, the highpass image 720 can go through coring 780 processing toreduce noise and artifacts.

The sigma filter 730 decomposes the input image into the lowpass andhighpass images. The sigma filter was first published by Lee (J. S. Lee,“Digital image enhancement and noise filtering by use of localstatistics,” in IEEE Trans. Pattern Analysis and Machine Intelligence,Vol. PAMI-2, No. 2, pp. 165-168, March, 1980). The sigma filter utilizesa 1-D or 2-D rectangular window, where the current pixel I(x,y) is atthe center of the window. The sigma filter compares all the pixelsI(i,j) in the window with the central pixel I(x,y), and only averagesthose pixels whose value differences with the central pixel I(x,y) iswithin a threshold T. The sigma filter is a nonlinear filter.Mathematically, the output of the sigma filter, I_(LP)(x,y), iscalculated by

${I_{LP}\left( {x,y} \right)} = \frac{\sum\limits_{{{{{({i,j})} \in E}\&}{{{I{({i,j})}} - {I{({x,y})}}}}} < T}{I\left( {i,j} \right)}}{N\left( {x,y} \right)}$

where E is the window; N(x,y) is the count of the pixels in E thatsatisfy the condition of |I(i,j)−I(x,y)|<T. The parameters of the sigmafilter, the widow E and the threshold T, may be chosen empirically.

The sigma filter generates the lowpass image 710, and the highpass image720 is obtained by subtraction 790. Because the sigma filter is asmoothing filter preserving sharp edges, the lowpass image generated bya sigma filter contains few details but contains sharp edges, and thehighpass image contains details/noises/artifacts but few sharp edges. Itis also to be understood that a similar technique may be used to protectskin tones when the gamut of the display is smaller than the input colorgamut.

The terms and expressions which have been employed in the foregoingspecification are used therein as terms of description and not oflimitation, and there is no intention, in the use of such terms andexpressions, of excluding equivalents of the features shown anddescribed or portions thereof, it being recognized that the scope of theinvention is defined and limited only by the claims which follow.

1. A method for enhancing the color of an image to be displayed on adisplay comprising: (a) using a processor to receive an image having aplurality of pixels where each of said pixels has a plurality of colorcomponents; (b) using said processor to modify said image with a firstgamut mapping to a first color gamut; (c) using said processor to modifysaid image with a second gamut mapping to a second color gamut, whereinsaid second color gamut is generally different than said first colorgamut; (d) using said processor to determine potential skin-tone pixelsof said image; (e) using said processor to modify said image based uponsaid first gamut mapping, said second gamut mapping, and said potentialskin-tone pixels.
 2. The method of claim 1 wherein said first gamutmapping leaves said image substantially unchanged.
 3. The method ofclaim 1 wherein said second gamut mapping substantially changes saidimage to match that of an input image calibration.
 4. The method ofclaim 1 wherein said second color gamut is generally larger than saidfirst color gamut.
 5. The method of claim 1 wherein said potentialskin-tone pixels are filtered with a color space filter.
 6. The methodof claim 1 wherein said first gamut mapping is independent ofskin-tones.
 7. The method of claim 6 wherein said second gamut mappingis independent of skin-tones.
 8. The method of claim 1 wherein saidsecond gamut mapping is independent of skin-tones.
 9. The method ofclaim 1 wherein the values associated with said potential skin-tonepixels are non-binary.
 10. The method of claim 1 wherein said potentialskin-tone pixels may be modified based upon a gain.
 11. The method ofclaim 10 wherein said gain is user adjustable.
 12. The method of claim 1wherein said potential skin-tone pixels may be modified based upon ahue.
 13. The method of claim 12 wherein said hue is user adjustable. 14.The method of claim 1 wherein said potential skin-tone pixels may bebased upon saturation.
 15. The method of claim 14 wherein saidsaturation is user adjustable.
 16. The method of claim 1 wherein saidskin-tone pixels are based upon two dimensional look up tables.
 17. Themethod of claim 1 wherein said skin-tone pixels are also based upon onedimensional look up tables.
 18. The method of claim 1 wherein the imageis divided into 6 regions of a color space.
 19. A method for enhancingthe color of an image to be displayed on a display comprising: (a) usinga processor to receive an image having a plurality of pixels where eachof said pixels has a plurality of color components; (b) using saidprocessor to selectively modify a pixel of said image with either afirst gamut mapping to a first color gamut or a second gamut mapping toa second color gamut, wherein said second color gamut is generallydifferent than said first color gamut, based upon the likelihood thatsaid pixel is a skin-tone pixel of said image.
 20. The method of claim19 wherein said first gamut mapping leaves said pixel substantiallyunchanged once displayed.
 21. The method of claim 20 wherein saidskin-tone pixel is filtered with a spatial filter.
 22. The method ofclaim 19 wherein said first gamut mapping is independent of skin-tones.23. The method of claim 22 wherein said second gamut mapping isindependent of skin-tones.
 24. The method of claim 19 wherein saidskin-tone pixel may be modified based upon a gain.
 25. The method ofclaim 24 wherein said gain is user adjustable.
 26. The method of claim19 wherein said skin-tone pixel may be modified based upon a hue. 27.The method of claim 26 wherein said hue is user adjustable.
 28. Themethod of claim 19 wherein said skin-tone pixel may be based uponsaturation.
 29. The method of claim 28 wherein said saturation is useradjustable.